code: def evaluate(model, loss_fn, dataloader, device="cpu"):
      loss = 0
      f1 = 0
      num_blocks = 0
      for subgraph in dataloader:
          subgraph = subgraph.to(device)
          label = subgraph.ndata["label"].to(device)
          feat = subgraph.ndata["feat"]
          logits = model(subgraph, feat)
  
          # compute loss
          loss += loss_fn(logits, label).item()
          predict = np.where(logits.data.cpu().numpy() >= 0.0, 1, 0)
          f1 += f1_score(label.cpu(), predict, average="micro")
          num_blocks += 1
  
      return f1 / num_blocks, loss / num_blocks
  
file_path: dgl/examples/pytorch/geniepath/ppi.py
imports: import argparse
  import numpy
  import torch
  import torch.optim
  from dgl.data import PPIDataset
  from dgl.dataloading import GraphDataLoader
  from model import GeniePath, GeniePathLazy
  from sklearn.metrics import f1_score
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, loss_fn, dataloader, device) and returns: unknown,  unknown